Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

Cotton futures showed resilience with gains despite early weakness, influenced by dollar and oil trends, CFTC positioning, and ICE stock changes.

Content

This dataset provides a comprehensive, consolidated collection of daily historical stock data for all companies included in the S&P 100 index. It is designed to be a clean and reliable resource for financial analysis, machine learning, and academic research.

Key Features

Consolidated Data: All data is combined into a single, easy-to-use CSV file, simplifying cross-company analysis.

Top U.S. Companies: Contains data for the 100 largest and most influential non-financial companies in the S&P 500.

Daily Updates: The dataset is updated daily.

Comprehensive Metrics: Each entry includes key OHLCV (Open, High, Low, Close, Volume) data points.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for the company on Yahoo Finance.

name: The full name of the company.

Open: The opening price of the stock in USD at market open.

High: The highest price the stock reached during the trading day in USD.

Low: The lowest price the stock reached during the trading day in USD.

Close: The final price of the stock at market close in USD.

Volume: The total volume of shares traded during the day.

Data Collection

The data for this dataset is sourced from the Yahoo Finance API using the yfinance Python library. The list of S&P 100 companies is sourced from a reliable financial resource to ensure accuracy and relevance.

Potential Use Cases

Financial Analysis: Analyze market trends, performance correlations, and historical volatility.

Machine Learning: Train models to predict stock prices, identify trading patterns, or classify market regimes.

Time Series Modeling: Forecast future stock movements using historical price and volume data.

Educational Projects: Use as a practical, real-world dataset for learning data science and finance.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

This dataset contains daily historical data of major financial instruments and indexes from January 1, 2015, to August 15, 2025 . It includes the following columns:

SPX – S&P 500 Index daily closing prices.

GLD – SPDR Gold Shares ETF daily adjusted closing prices.

USO – United States Oil Fund ETF daily adjusted closing prices.

SLV – iShares Silver Trust ETF daily adjusted closing prices.

EUR/USD – Daily Euro to US Dollar exchange rate.

The data was collected from Yahoo Finance using the yfinance Python library. The dataset is intended for research, analysis, and educational purposes.

Context

Historically, gold had been used as a form of currency in various parts of the world including the USA. In present times, precious metals like gold are held with central banks of all countries to guarantee re-payment of foreign debts, and also to control inflation which results in reflecting the financial strength of the country. Recently, emerging world economies, such as China, Russia, and India have been big buyers of gold, whereas the USA, SoUSA, South Africa, and Australia are among the big seller of gold.

Forecasting rise and fall in the daily gold rates can help investors to decide when to buy (or sell) the commodity. But Gold prices are dependent on many factors such as prices of other precious metals, prices of crude oil, stock exchange performance, Bonds prices, currency exchange rates, etc.

The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value.

Content

Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The historical data of Gold ETF fetched from Yahoo finance has 7 columns, Date, Open, High, Low, Close, Adjusted Close, and Volume, the difference between Adjusted Close and Close is that the closing price of a stock is the price of that stock at the close of the trading day. Whereas the adjusted closing price takes into account factors such as dividends, stock splits, and new stock offerings to determine a value. So, Adjusted Close is the outcome variable which is the value you have to predict.

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Acknowledgements

The data is collected from Yahoo finance.

Inspiration

Can you predict Gold prices accurately using traditional machine learning algorithms

🪙 Bitcoin & Macro Indicators – Daily Dataset (2013–2025)

This dataset consolidates daily data of Bitcoin (BTC) prices together with key macroeconomic and market sentiment indicators — gold, volatility (VIX), and the U.S. dollar strength (UUP). It is designed for financial modeling and machine learning, especially for studying BTC price behavior relative to global risk variables.

📄 File: df_all_for_kaggle.csv

Each row represents one business day (Monday to Friday). Weekend and holiday gaps have been forward-filled for non-crypto variables to maintain a consistent calendar.

📊 Column descriptions Column Description date (index) Date of observation (business day). close_usd Daily closing price of Bitcoin (BTC-USD) in USD. gold_close Closing price of GLD ETF (proxy for gold price). vix_close Closing value of the CBOE Volatility Index (VIX). usd_close Closing price of UUP ETF, which tracks the strength of the U.S. Dollar Index (DXY). btc_weekend_ret Accumulated BTC return over Saturday and Sunday, assigned to the following Monday. Values are in decimals (e.g., 0.05 = +5%). ret_btc_next_bday Next-day BTC return (close / close − 1), expressed in decimals. y_up_next Binary variable: 1 if BTC rises the next business day, 0 otherwise. mom_14c 14-day calendar momentum of BTC (close / close − 1). Captures medium-term trend including weekends. ⚙️ Methodology summary

BTC data from Yahoo Finance (BTC-USD), covering 2013–2025.

Gold (GLD), volatility (^VIX), and dollar (UUP) from Yahoo Finance as well.

All series merged on business-day frequency (asfreq("B")).

Non-BTC assets are forward-filled on weekends to align calendars.

Momentum and return variables are computed in decimal format, not percentage points.

Missing data at the start of rolling windows (e.g., first 14 days of momentum) are NaN

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

Gold dataset is created by calling API from Fred and Yahoo Finance. It contains 4517 rows x 11 columns: 1.Unnamed: 0 →

Likely represents the Date of observation.

Format: MM/DD/YYYY.

2.Gold →

The gold price in U.S. dollars per troy ounce.

Gold is a safe-haven asset often used to hedge against inflation and currency risk.

3.USD_Index →

The U.S. Dollar Index (DXY).

Measures the value of the U.S. dollar against a basket of six major currencies (EUR, JPY, GBP, CAD, SEK, CHF).

Used to gauge dollar strength globally.

4.Oil →

The crude oil price in U.S. dollars per barrel.

Likely West Texas Intermediate (WTI) benchmark.

Important for global energy markets and inflation.

5.Silver →

The silver price in U.S. dollars per troy ounce.

Like gold, silver is a precious metal used both as an investment and in industry.

6.SP500 →

The S&P 500 Index.

A stock market index that tracks the performance of 500 of the largest publicly traded companies in the U.S.

A key indicator of overall U.S. stock market performance.

7.Bitcoin →

The Bitcoin price in U.S. dollars.

First decentralized cryptocurrency, highly volatile.

Note: Missing data before 2011 since Bitcoin did not exist in markets before then.

8.Interest_Rate →

The U.S. Federal Funds Effective Rate (%).

The short-term interest rate at which banks lend to each other overnight.

Set by the Federal Reserve as a key monetary policy tool.

9.10Y_Treasury_Yield →

The yield (%) on U.S. Treasury Bonds with a 10-year maturity.

Reflects government borrowing costs and investor expectations for inflation and growth.

Often seen as the “risk-free rate” benchmark.

10.Inflation_CPI →

The Consumer Price Index (CPI).

Measures the average change in prices paid by consumers for goods and services (inflation indicator).

Higher CPI → higher inflation.

11.Unemployment →

The U.S. unemployment rate (%).

Measures the percentage of the labor force that is jobless but actively seeking work.

Key economic health indicator.

Overview This dataset provides daily stock price data for NVIDIA (NVDA) from January 2014 to March 2024, including key volatility indicators. It is sourced from Yahoo Finance (yfinance) and can be used for stock market analysis, time series forecasting, and financial modeling.

Data Summary Time Period: January 2014 – March 2024 (10 years) Stock Symbol: NVDA (NVIDIA Corporation) Data Source: Yahoo Finance (yfinance) Update Frequency: Not updated (historical dataset)

### ## Column Name Description Date Trading date (YYYY-MM-DD) Open Stock opening price (USD) High Highest price of the day (USD) Low Lowest price of the day (USD) Close Closing price of the stock (USD) Volume Number of shares traded Daily_Return Percentage change in stock price from the previous day Rolling_Volatility 20-day rolling standard deviation of Daily_Return ATR (Average True Range) Measures daily price range fluctuations (14-day window) Rolling_Mean 20-day moving average of the Close price Upper_Band Upper Bollinger Band (20-day mean + 2 standard deviations) Lower_Band Lower Bollinger Band (20-day mean - 2 standard deviations)